Apparatus and method for adjusting a user nourishment selection based on nutrient diversity

ABSTRACT

An apparatus and method for adjusting a user nourishment selection based on nutrient diversity, the apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a proposed user selection relating to nourishment, wherein the proposed user selection comprises a plurality of ingredients, evaluate each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes extracting at least a nutrient from each ingredient of the plurality of ingredients and calculating a nutrient biodiversity score for the at least a nutrient, optimize the plurality of ingredients as a function of each nutrient biodiversity score, and adjust the plurality of ingredients as a function of the optimization of the plurality of ingredients.

FIELD OF THE INVENTION

The present invention generally relates to the field of toxic loads. Inparticular, the present invention is directed to an apparatus and methodfor adjusting a user nourishment selection based on nutrient diversity.

BACKGROUND

Consuming a variety of foods with diverse nutrients increases diversityin the human microbiome and overall health.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for adjusting a user nourishment selectionbased on nutrient diversity is shown. The apparatus comprising at leasta processor and a memory communicatively connected to the at least aprocessor, the memory containing instructions configuring the at least aprocessor to receive a proposed user selection relating to nourishment,wherein the proposed user selection comprises a plurality ofingredients, evaluate each ingredient of the plurality of ingredients,wherein evaluating each ingredient includes extracting at least anutrient from each ingredient of the plurality of ingredients andcalculating a nutrient biodiversity score for the at least a nutrient,optimize the plurality of ingredients as a function of each nutrientbiodiversity score, and adjust the plurality of ingredients as afunction of the optimization of the plurality of ingredients.

In another aspect, a method for adjusting a user nourishment selectionbased on nutrient diversity is presented. The method comprisesreceiving, at the at least a processor, a proposed user selectionrelating to nourishment, wherein the proposed user selection comprises aplurality of ingredients, evaluating, at the at least a processor, eachingredient of the plurality of ingredients, wherein evaluating eachingredient includes extracting at least a nutrient from each ingredientof the plurality of ingredients and calculating a nutrient biodiversityscore for the at least a nutrient, optimizing, at the at least aprocessor, the plurality of ingredients as a function of each nutrientbiodiversity score, and adjusting, at the at least a processor, theplurality of ingredients as a function of the optimization of theplurality of ingredients.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is an exemplary embodiment of a block diagram of an apparatus foradjusting a user nourishment selection based on nutrient diversity;

FIG. 2 is an exemplary embodiment of an ingredient database;

FIG. 3 is an exemplary embodiment of a fuzzy logic system;

FIG. 4 is an exemplary embodiment of a block diagram of a machineleaning model;

FIG. 5 is an exemplary embodiment of a flow diagram for a method foradjusting a user nourishment selection based on nutrient diversity; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatuses and methods for adjusting a user nourishment selection basedon nutrient diversity. Aspects of the present disclosure may include atleast a processor and a memory communicatively connected to the at leasta processor. Aspects of the present disclosure may include the memorycontaining instructions configuring the at least a processor to receivea proposed user selection relating to nourishment, wherein the proposeduser selection comprises a plurality of ingredients. Aspects of thepresent disclosure may include evaluating each ingredient of theplurality of ingredients, wherein evaluating each ingredient includesextracting at least a nutrient from the ingredient and calculating anutrient biodiversity score for the at least a nutrient. Aspects of thepresent disclosure may include optimizing the plurality of ingredientsas a function of each nutrient biodiversity score. Aspects of thepresent disclosure may include adjusting the plurality of ingredients asa function of the optimization of the plurality of ingredients.

Now referring to FIG. 1 , an apparatus 100 for adjusting a usernourishment selection based on nutrient diversity is presented in theblock diagram. Apparatus 100 may include at least a processor 104 and amemory communicatively connected to the at least a processor 104. Asused in this disclosure, “communicatively connected” means connected byway of a connection, attachment or linkage between two or more relatawhich allows for reception and/or transmittance of informationtherebetween. For example, and without limitation, this connection maybe wired or wireless, direct or indirect, and between two or morecomponents, circuits, devices, systems, and the like, which allows forreception and/or transmittance of data and/or signal(s) therebetween.Data and/or signals therebetween may include, without limitation,electrical, electromagnetic, magnetic, video, audio, radio and microwavedata and/or signals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure.

With continued reference to FIG. 1 , a memory may contain instructionsconfiguring the at least a processor 104 to perform various tasks.Apparatus 100 may include any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Apparatus 100 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Apparatus 100 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. Apparatus 100 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting apparatus 100 to one or more of a varietyof networks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Apparatus 100 may include butis not limited to, for example, a computing device or cluster ofcomputing devices in a first location and a second computing device orcluster of computing devices in a second location. Apparatus 100 mayinclude one or more computing devices dedicated to data storage,security, distribution of traffic for load balancing, and the like.Apparatus 100 may distribute one or more computing tasks as describedbelow across a plurality of computing devices of computing device, whichmay operate in parallel, in series, redundantly, or in any other mannerused for distribution of tasks or memory between computing devices.Apparatus 100 may be implemented using a “shared nothing” architecturein which data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

With continued reference to FIG. 1 , apparatus 100 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, apparatus 100 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Apparatus 100 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1 , at least a processor 104 is configured toreceive a proposed user selection 108 relating to nourishment. A “userselection” as described in this disclosure, is data describing a user'spreference in regard to any source of nourishment, including any foodand/or beverage consumed by a human being. Proposed user selection 108may indicate a user's like or dislike of an ingredient, a meal, a drinkor beverage, and the like. Proposed user selection 108 may furtherinclude a recipe, meal, snack, or any sort of combination of nourishmentingredients that a user may consume. Proposed user selection 108comprises a plurality of ingredients 112. A “plurality of ingredients”refers to a collection of food or substances that are combined to make adish or recipe for a user to consume. For instance and withoutlimitation, proposed user selection 108 may indicate that a user likesingredients such as avocado, salmon, and jasmine rice, but the userdislikes black olives. In yet another non-limiting example, proposeduser selection 108 may indicate that a user likes meals that includechicken alfredo, chicken parmesan, and spaghetti and meatballs, but theuser dislikes meals that contain fish including fish tacos and pansautéed cod. Proposed user selection 108 may indicate a user's eatingpatterns including the number of meals a user eats each day, the timesof the day the user prefers to eat meals, meals a user skips or does noteat, number of snacks a user consumes each day and the like. Proposeduser selection 108 may indicate a nutrient deficiency, which is wherethe microbiome of the user lacks an essential nutrient needed forgrowth. Proposed user selection 108 may indicate a user's cooking andmeal preparation patterns, including if a user cooks meals at home,orders meal preparation kits, orders prepared foods, shops for groceriesonline or in person at a grocery store, eats at restaurants, and thelike. Proposed user selection 108 may indicate a user's meal andingredient source, such as if a user prefers ingredients that do notcontain genetically modified organisms (GMOs), if a user prefers seafoodthat is wild caught and sustainable, if a user prefers free-rangepoultry, and/or if a user prefers organically sourced produce forexample. Information pertaining to proposed user selection 108 may bestored in a database, as described herein with reference to FIG. 2 .Furthermore, receiving proposed user selection 108 relating tonourishment may include receiving a user flavor preference. As usedherein, “user flavor preference” is ingredients or nutrients the userprefers over other ingredients and nutrients. User flavor preferencemay, without indication, that the user's favorite type of fruit isstrawberries, the user is allergic to spinach, the user does not likethe taste of seafood, or anything similar.

Referring still to FIG. 1 , proposed user selection 108 may be receivedas a function of a biological extraction. As used in this disclosure“biological extraction” is at least an element of user biological data.As used in this disclosure, “biological data” is data indicative of aperson's biological state; biological state may be evaluated with regardto one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Biological extraction may includeat least a marker associated with a biochemical status of an individual.As used in this disclosure a “marker” is a biochemical datum that maypertain to a biochemical status of an individual. For instance, andwithout limitation, the marker may include a particular set ofbiomarkers, test results, and/or biochemical information that isrecognized in a given medical field as useful for identifyingbiochemical statuses of individuals within a relevant field. As anon-limiting example, and without limitation, marker describing redblood cells, such as red blood cell count, hemoglobin levels,hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, and/ormean corpuscular hemoglobin concentration may be recognized as usefulfor identifying various conditions such as dehydration, hightestosterone, nutrient deficiencies, kidney dysfunction, chronicinflammation, anemia, and/or blood loss. Biological extraction data mayalternatively or additionally include any data used as a biologicalextraction as described in U.S. Nonprovisional application Ser. No.16/502,835, filed on Jul. 3, 2019, and entitled “METHODS AND SYSTEMS FORACHIEVING VIBRANT CONSTITUTION BASED ON USER INPUTS,” the entirety ofwhich is incorporated herein by reference. Additionally oralternatively, biochemical profile 108 may be determined according toany processes used as a determination process as described in U.S.Nonprovisional application Ser. No. 16/502,835 filed Jul. 3, 2019 andtitled “METHODS AND SYSTEMS FOR ACHIEVING VIBRANT CONSTITUTION BASED ONUSER INPUTS”, the entirety of which is referenced herein.

Still referring to FIG. 1 , biological extraction may be identified as afunction of one or more monitoring devices. As used in this disclosure“monitoring device” is an electronic device that is worn on the personof a user, such as without limitation close to and/or on the surface ofthe skin, wherein the device can detect, analyze, and transmitbiochemical information concerning an individual. The monitoring devicemay include, without limitation, any device that further collects,stores, and analyzes data associated with a biochemical profile. Themonitoring device my consist of, without limitation, near-bodyelectronics, on-body electronics, in-body electronics, electronictextiles, smart watches, smart glasses, smart clothing, fitnesstrackers, body sensors, wearable cameras, head-mounted displays, bodyworn cameras, Bluetooth headsets, wristbands, smart garments, cheststraps, sports watches, fitness monitors, and the like thereof. Themonitoring device may include directed light monitoring devices such asspectrophotometric device at least identify concentrations of markersand/or identify one or more user biochemical statuses such as body massindex, fat percentage, water percentage, bone mass percentage, musclemass percentage, and the like thereof. The monitoring device mayinclude, without limitation, earphones, earbuds, headsets, bras, suits,jackets, trousers, shirts, pants, socks, bracelets, necklaces, brooches,rings, jewelry, AR HMDs, VR HMDs, exoskeletons, location trackers, andgesture control wearables. The monitoring device may include one or moremedical devices that are operated by one or more informed advisors,wherein an informed advisor may include any medical professional who mayassist and/or participate in the medical treatment of a user. Aninformed advisor may include a medical doctor, nurse, physicianassistant, pharmacist, yoga instructor, nutritionist, spiritual healer,meditation teacher, fitness coach, health coach, life coach, and thelike. As a non-limiting example, a medical device of a stethoscope,ultrasound device, MRI device, PET scanner, CT scanner, X-ray device,electrocardiogram device, and the like thereof.

Moreover, and still referring to FIG. 1 , biological extraction mayinclude at least an element of user physiological data. As used in thisdisclosure, “physiological data” is any data indicative of a person'sphysiological state; physiological state may be evaluated with regard toone or more measures of health of a person's body, one or more systemswithin a person's body such as a circulatory system, a digestive system,a nervous system, or the like, one or more organs within a person'sbody, and/or any other subdivision of a person's body useful fordiagnostic or prognostic purposes. For instance, and without limitation,a particular set of biomarkers, test results, and/or biochemicalinformation may be recognized in a given medical field as useful foridentifying various disease conditions or prognoses within a relevantfield. As a non-limiting example, and without limitation, physiologicaldata describing red blood cells, such as red blood cell count,hemoglobin levels, hematocrit, mean corpuscular volume, mean corpuscularhemoglobin, and/or mean corpuscular hemoglobin concentration may berecognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss.

With continued reference to FIG. 1 , physiological state data mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which ieasures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, Without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1 , physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbAlc)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline phosphatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibrinogen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1 , physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1 , physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1 , physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using any machine-learning and/or language processingmodule as described in this disclosure.

Still referring to FIG. 1 , physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences contained in one or morechromosomes in human cells. Genomic data may include, withoutlimitation, ribonucleic acid (RNA) samples and/or sequences, such assamples and/or sequences of messenger RNA (mRNA) or the like taken fromhuman cells. Genetic data may include telomere lengths. Genomic data mayinclude epigenetic data including data describing one or more states ofmethylation of genetic material. Physiological state data may includeproteomic data, which as used herein is data describing all proteinsproduced and/or modified by an organism, colony of organisms, or systemof organisms, and/or a subset thereof. Physiological state data mayinclude data concerning a microbiome of a person, which, as used herein,includes any data describing any microorganism and/or combination ofmicroorganisms living on or within a person, including withoutlimitation biomarkers, genomic data, proteomic data, and/or any othermetabolic or biochemical data useful for analysis of the effect of suchmicroorganisms on other physiological state data of a person, asdescribed in further detail below.

With continuing reference to FIG. 1 , physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.

With continued reference to FIG. 1 , physiological data may include,without limitation any result of any medical test, physiologicalassessment, cognitive assessment, psychological assessment, or the like.Apparatus 100 may receive at least a physiological data from one or moreother devices after performance; apparatus 100 may alternatively oradditionally perform one or more assessments and/or tests to obtain atleast a physiological data, and/or one or more portions thereof, onapparatus 100. For instance, at least physiological data may include ormore entries by a user in a form or similar graphical user interfaceobject; one or more entries may include, without limitation, userresponses to questions on a psychological, behavioral, personality, orcognitive test. For instance, apparatus 100 may present to a user a setof assessment questions designed or intended to evaluate a current stateof mind of the user, a current psychological state of the user, apersonality trait of the user, or the like; apparatus 100 may provideuser-entered responses to such questions directly as at least aphysiological data and/or may perform one or more calculations or otheralgorithms to derive a score or other result of an assessment asspecified by one or more testing protocols, such as automatedcalculation of a Stanford-Binet and/or Wechsler scale for IQ testing, apersonality test scoring such as a Myers-Briggs test protocol, or otherassessments that may occur to persons skilled in the art upon reviewingthe entirety of this disclosure.

With continued reference to FIG. 1 , assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device; third-party device may include, without limitation,a server or other device (not shown) that performs automated cognitive,psychological, behavioral, personality, or other assessments.Third-party device may include a device operated by an informed advisor.An informed advisor may include any medical professional who may assistand/or participate in the medical treatment of a user. An informedadvisor may include a medical doctor, nurse, physician assistant,pharmacist, yoga instructor, nutritionist, spiritual healer, meditationteacher, fitness coach, health coach, life coach, and the like.

With continued reference to FIG. 1 , physiological data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1 , physiological data may include oneor more user body measurements. A “user body measurement” as used inthis disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or autoimmune disorders.

With continued reference to FIG. 1 , user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1 , epigenetic, as used herein,includes any user body measurements describing changes to a genome thatdo not involve corresponding changes in nucleotide sequence. Epigeneticbody measurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior patterns. This may include effects on cellularand physiological phenotypic traits that may occur due to external orenvironmental factors. For example, DNA methylation and histonemodification may alter phenotypic expression of genes without alteringunderlying DNA sequence. Epigenetic body measurements may include datadescribing one or more states of methylation of genetic material.

With continued reference to FIG. 1 , gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1 , gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,campylobacter species, Clostridium difficile, cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, Candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-400. Gut-wallbody measurement may include data describing one or more images such asx-ray, MRI, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, MRI fluoroscopy,positron emission tomography 9PET), diffusion-weighted MRI imaging, andthe like.

With continued reference to FIG. 1 , microbiome, as used herein,includes ecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests,methane-based breath tests, hydrogen based breath tests, fructose basedbreath tests. Helicobacter pylori breath test, fructose intolerancebreath test, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

Nutrients may include carbohydrates, protein, lipids, vitamins,minerals, antioxidants, fatty acids, amino acids, and the like.Nutrients may include for example vitamins such as thiamine, riboflavin,niacin, pantothenic acid, pyridoxine, biotin, folate, cobalamin, VitaminC, Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1 , nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1 , genetic as used herein, includesany inherited trait. Inherited traits may include genetic materialcontained with DNA including for example, nucleotides. Nucleotidesinclude adenine (A), cytosine (C), guanine (G), and thymine (T). Geneticinformation may be contained within the specific sequence of anindividual's nucleotides and sequence throughout a gene or DNA chain.Genetics may include how a particular genetic sequence may contribute toa tendency to develop a certain disease such as cancer or Alzheimer'sdisease.

With continued reference to FIG. 1 , genetic body measurement mayinclude one or more results from one or more blood tests, hair tests,skin tests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1 genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1 , genetic body measurement mayinclude an analysis of COMT gene that is responsible for producingenzymes that metabolize neurotransmitters. Genetic body measurement mayinclude an analysis of DRD2 gene that produces dopamine receptors in thebrain. Genetic body measurement may include an analysis of ADRA2B genethat produces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vaso-dilation and vaso-constriction of blood vessels.

With continued reference to FIG. 1 , genetic body measurement mayinclude ACE gene that is involved in producing enzymes that regulateblood pressure. Genetic body measurement may include SLCO1B1 gene thatdirects pharmaceutical compounds such as statins into cells. Geneticbody measurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fullness after eating. Genetic body measurementmay include MC4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1 , genetic body measurement mayinclude CYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1 , metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1 , physiological data may be obtainedfrom a physically extracted sample. A “physical sample” as used in thisexample, may include any sample obtained from a human body of a user. Aphysical sample may be obtained from a bodily fluid and/or tissueanalysis such as a blood sample, tissue, sample, buccal swab, mucoussample, stool sample, hair sample, fingernail sample and the like. Aphysical sample may be obtained from a device in contact with a humanbody of a user such as a microchip embedded in a user's skin, a sensorin contact with a user's skin, a sensor located on a user's tooth, andthe like. Physiological data may be obtained from a physically extractedsample. A physical sample may include a signal from a sensor configuredto detect physiological data of a user and record physiological data asa function of the signal. A sensor may include any medical sensor and/ormedical device configured to capture sensor data concerning a patient,including any scanning, radiological and/or imaging device such aswithout limitation x-ray equipment, computer assisted tomography (CAT)scan equipment, positron emission tomography (PET) scan equipment, anyform of magnetic resonance imagery (MRI) equipment, ultrasoundequipment, optical scanning equipment such as photo-plethysmographicequipment, or the like. A sensor may include any electromagnetic sensor,including without limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. A sensor may include atemperature sensor. A sensor may include any sensor that may be includedin a mobile device and/or wearable device, including without limitationa motion sensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a wearable and/ormobile device sensor may detect heart rate or the like. A sensor maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Asensor may be configured to detect internal and/or external biomarkersand/or readings. A sensor may be a part of apparatus 100 or may be aseparate device in communication with apparatus 100.

Referring still to FIG. 1 , at least a processor 104 is furtherconfigured to evaluate each ingredient of the plurality of ingredients112. Evaluating each ingredient of the plurality of ingredients 112includes extracting at least a nutrient 116 from the ingredient.Ingredients are nutrition elements, wherein a “nutrition element,” asused in this disclosure, is an item that includes at least a nutrient116 intended to be used and/or consumed by a user. A “nutrient,” as usedin this disclosure,” is a biologically active compound substance whoseconsumption provides nourishment essential for growth and themaintenance of life in a microbiome. Types of at least a nutrient 116may include, without limitation, carbohydrates, proteins, fats,vitamins, minerals, dietary fiber, water, or the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

Continuing to refer to FIG. 1 , evaluating each ingredient of theplurality of ingredients 112 includes calculating a nutrientbiodiversity score 120 for the at least a nutrient 116. A “nutrientbiodiversity score” is data including any character, symbolic, and/ornumerical data, reflecting the diversity of the nutrient compared to theuser's current diet and health status; in other words, the score relayshow diverse the nutrient is amongst the nutrients the user wouldnormally eat in their diet. Nutrient biodiversity score 120 representsthe effect of the at least a nutrient 116 on the diversity of amicrobiome. Nutrient biodiversity score 120 may be transient and/ordynamic. Nutrient biodiversity score 120 may be updated based on one ormore meals that a user consumed and/or is planning to consume. Nutrientbiodiversity score 120 may be calculated by at least a processor 104 byretrieving information contained within performance character database136. Nutrient biodiversity score 120 may be graded on a continuum, wherea score of zero may indicate a user who is in extremely poor nutritionalhealth while a score of 100 may indicate a user who is in excellentnutritional health. Nutrient biodiversity score 120 may be calculatedfrom one or more factors that may be stored within performance characterdatabase 136 such as food intake, water intake, supplement intake,prescription medication intake, fitness practice, health goals, chronichealth conditions, acute health conditions, spiritual wellness,meditation practice, stress levels, and the like.

With continued reference to FIG. 1 , calculating a nutrient biodiversityscore 120 may include the use of a biodiversity machine-learning model124. Calculating nutrient biodiversity score 120 also includesretrieving at least a nutrient containing a logged biodiversity entry.At least a processor 104 may then generate a biodiversitymachine-learning model 124, wherein the biodiversity machine-learningmodel utilizes the logged biodiversity entry as an input, and outputsthe nutrient biodiversity. A “logged nourishment entry,” as used in thisdisclosure, is any stored factor that is utilized to calculate anutrient biodiversity score 120. A logged nourishment entry may includea user's daily water intake, a user's supplement intake, and the like asdescribed below in more detail. A logged nourishment entry may include anourishment behavioral target. A “nourishment behavioral target,” asused in this disclosure, is a user behavior goal relating to nourishmentpossibilities. A behavior goal may include a desire to cook a certainnumber of meals at home each week, or a desire to only eat fast food acertain number of times each month. A behavior goal may be self-reportedby a user, and a user's progress towards meeting the behavior goal maybe calculated into a user's nutrient biodiversity score 120. Forexample, a user who continues to not achieve any progress towards auser's nourishment behavior target to eat fish at least three times eachweek may decrease a user's overall nutrient biodiversity score 120,while a user with the same nourishment behavior target and who doescontinuously eat fish three times each week may increase the user'soverall nutrient biodiversity score 120. A behavior goal may relate to afood source, such as a desire to only go out to eat no more than threedays each week and to eat the rest of a user's meals at home. A behaviorgoal may relate to a food option such as to only consume foods that donot contain genetically modified organisms, or to only consume foodsthat do not contain high fructose corn syrup. Nutrient biodiversityscore 120 may be affected by, without limitation, seasonal nutrients,allergies, local nutrients, or the like.

One or more logged nourishment entries may be stored in a database asdescribed herein with reference to FIG. 2 . At least a processor 104 mayretrieve one or more elements of data containing a logged nourishmententry such as by generating a query, including any of the queries asdescribed herein. At least a processor 104 generates a biodiversitymachine-learning process. A “machine-learning process,” as used in thisdisclosure, is a process that automatedly uses a body of data known as“training data” and/or a “training set” to generate an algorithm thatwill be performed by at least a processor 104 and/or a module to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage. As used herein, training data used to train biodiversitymachine-learning model 124 is biodiversity training data 128.Biodiversity training data 128 may input any of the data describedherein from proposed user selection relating to nourishment or adatabase, and outputs a biodiversity nutrient score. A “biodiversitymachine-learning process,” as used in this disclosure, is anymachine-learning process that utilizes a logged nourishment entry as aninput, and outputs a biodiversity nutrient score 120. “Training data,”as used in this disclosure, is data containing correlations that amachine-learning process including a machine-learning algorithm and/ormachine-learning process may use to model relationships between two ormore categories of data elements. Training data may be formatted toinclude labels, for instance by associating data elements with one ormore descriptors corresponding to categories of data elements. Trainingdata may not contain labels, where training data may not be formatted toinclude labels. Biodiversity machine-learning process may be generatedcalculating one or more machine-learning algorithms and/or producing oneor more machine-learning models.

With continued reference to FIG. 1 , a machine-learning model mayinclude one or more supervised machine-learning algorithms, which mayinclude active learning, classification, regression, analyticallearning, artificial neural network, backpropagation, boosting, Bayesianstatistics, case-based learning, genetic programming, Kernel estimators,naïve Bayes classifiers, maximum entropy classifier, conditional randomfield, K-nearest neighbor algorithm, support vector machine, randomforest, ordinal classification, data pre-processing, statisticalrelational learning, and the like. A machine-learning algorithm mayinclude an unsupervised machine-learning algorithm, that is trainedusing training data that does not contain data labels. An unsupervisedmachine-learning algorithm may include a clustering algorithm such ashierarchical clustering, k-means clustering, mixture models, densitybased spatial clustering of algorithms with noise (DBSCAN), orderingpoints to identify the clustering structure (OPTICS), anomaly detectionsuch as local outlier factor, neural networks such as autoencoders, deepbelief nets, Hebbian learning, generative adversarial networks,self-organizing map, and the like. A machine-learning algorithm mayinclude semi-supervised learning that may be trained using training datathat contains a mixture of labeled and unlabeled data. Amachine-learning algorithm may include reinforcement learning,self-learning, feature learning, sparse dictionary learning, anomalydetection, robot learning, association rules, and the like. Amachine-learning algorithm may include generating one or moremachine-learning models. A “machine-learning model,” as used in thisdisclosure, is any mathematical representation of a relationship betweeninputs and outputs. A machine-learning model an artificial neuralnetwork, a decision tree, a support vector machine, regression analysis,Bayesian network, genetic algorithms, and the like.

Still referring to FIG. 1 , calculating a nutrient biodiversity scoremay further include the use of a biodiversity classifier 132. A“classifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. Biodiversity classifier 132 may be configured tooutput at least a datum that labels or otherwise identifies a set ofdata that are clustered together, found to be close under a distancemetric as described below, or the like. At least a processor 104 and/oranother device may generate a biodiversity classifier 132 using aclassification algorithm, defined as a processes whereby a at least aprocessor 104 derives a classifier from training data. Biodiversityclassifier may receive at least a nutrient 116 as input and output anutrient biodiversity score 120. Training data for biodiversityclassifier 132 may include any of the inputs and outputs describedabove. Training data for biodiversity classifier 132 may includenutrients correlated to nutrient biodiversity scores. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naïve Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 1 , at least a processor 104 may be configuredto generate a biodiversity classifier 132 using a Naïve Bayesclassification algorithm. Naïve Bayes classification algorithm generatesclassifiers by assigning class labels to problem instances, representedas vectors of element values. Class labels are drawn from a finite set.Naïve Bayes classification algorithm may include generating a family ofalgorithms that assume that the value of a particular element isindependent of the value of any other element, given a class variable.Naïve Bayes classification algorithm may be based on Bayes Theoremexpressed as P(A/B)=P(B/A) P(A)−P(B), where P(A/B) is the probability ofhypothesis A given data B also known as posterior probability; P(B/A) isthe probability of data B given that the hypothesis A was true; P(A) isthe probability of hypothesis A being true regardless of data also knownas prior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. At least aprocessor 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels. Atleast a processor 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , at least a processor 104 may beconfigured to generate a biodiversity classifier 132 using a K-nearestneighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used inthis disclosure, includes a classification method that utilizes featuresimilarity to analyze how closely out-of-sample-features resembletraining data to classify input data to one or more clusters and/orcategories of features as represented in training data; this may beperformed by representing both training data and input data in vectorforms, and using one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute/as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Classification may also occur as a function of a fuzzy inference systemor doing some sort of average similarity score. “Fuzzy inference” is theprocess of formulating a mapping from a given input to an output usingfuzzy logic. “Fuzzy logic” is a form of many-valued logic in which thetruth value of variables may be any real number between 0 and 1. Fuzzylogic may be employed to handle the concept of partial truth, where thetruth value may range between completely true and completely false. Themapping of a given input to an output using fuzzy logic may provide abasis from which decisions may be made and/or patterns discerned. Afirst fuzzy set may be represented, without limitation, according to afirst membership function representing a probability that an inputfalling on a first range of values is a member of the first fuzzy set,where the first membership function has values on a range ofprobabilities such as without limitation the interval [0,1], and an areabeneath the first membership function may represent a set of valueswithin the first fuzzy set. A first membership function may include anysuitable function mapping a first range to a probability interval,including without limitation a triangular function defined by two linearelements such as line segments or planes that intersect at or below thetop of the probability interval. “Linguistic variables” may, in anon-limiting example, cover input value factors and the “defuzzified”output may represent a score or output indicating how likely a missionis to succeed or, via a functional output or threshold comparison, beused to make the “go/no go” determination. Linguistic variables mayrepresent, for instance, degree of charge of batteries, externaltemperature, wind velocity, or any other variable that may affect aprobability of successful completion of a flight. Combinations of inputvariables and/or member functions may be linked to and/or composed withoutput membership functions and/or functional output formulas such asTSK functions to generate a defuzzified probability of success, and/orscore to be compared to a threshold. Any parameters, biases, weights orcoefficients of membership functions may be tuned and/or trained usingmachine-learning algorithms as described in this disclosure. Fuzzyinferencing and logic is further described herein with reference to FIG.3 .

Still referring to FIG. 1 , at least a processor 104 is also configuredto optimize the plurality of ingredients 112 as a function of eachnutrient biodiversity score 120. As used herein, “optimize” means torearrange or rewrite to improve efficiency of processing. Optimizingplurality of ingredients 112 may include creating a list of theplurality of ingredients 112 and organizing list of the plurality ofingredients 112 based on the nutrient biodiversity score 120 of eachingredient. Organizing a list of plurality of ingredients 112 mayinclude arranging them in order from lowest nutrient biodiversity score120 to highest nutrient biodiversity score 120, or vice versa.Additionally, without limitation, organizing the list may includeorganizing the list from most present in the user's microbiome to leastpresent or vice versa. List of ingredients may be organized in anyway inorder to figure out which ingredients will increase the biodiversity ofthe user's microbiome.

Referring still to FIG. 1 , at least a processor 104 is furtherconfigured to adjust the plurality of ingredients 112 as a function ofthe optimization of the plurality of ingredients 112. Ingredientadjustment 136 is an action of adjusting the plurality of ingredients112. As used herein, an “adjustment” is any change in the originalplurality of ingredients, including, without limitation, a change inamounts of ingredients, exchange of ingredients, or the like. Adjustingplurality of ingredients 112 may include replacing at least aningredient of the plurality of ingredients 112. Also, adjustingplurality of ingredients 112 may further include maximizing thediversity of nutrients in the plurality of ingredients.

Furthermore and still referring to FIG. 1 , at least a processor 104 mayuse a language processing module at any of the steps explained herein. Alanguage processing module may include any hardware and/or softwaremodule. A language processing module may be configured to extract, fromthe one or more documents, one or more words. One or more words mayinclude, without limitation, strings of one or more characters,including without limitation any sequence or sequences of letters,numbers, punctuation, diacritic marks, engineering symbols, geometricdimensioning and tolerancing (GD&T) symbols, chemical symbols andformulas, spaces, whitespace, and other symbols, including any symbolsusable as textual data as described above. Textual data may be parsedinto tokens, which may include a simple word (sequence of lettersseparated by whitespace) or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

A language processing module may operate to produce a languageprocessing model. Language processing model may include a programautomatically generated by computing device and/or language processingmodule to produce associations between one or more words extracted fromat least a document and detect associations, including withoutlimitation mathematical associations, between such words. Associationsbetween language elements, where language elements include for purposesherein extracted words, relationships of such categories to other suchterm may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of semantic meaning. As a further example, statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating a positive and/or negativeassociation between at least an extracted word and/or a given semanticmeaning; positive or negative indication may include an indication thata given document is or is not indicating a category semantic meaning.Whether a phrase, sentence, word, or other textual element in a documentor corpus of documents constitutes a positive or negative indicator maybe determined, in an embodiment, by mathematical associations betweendetected words, comparisons to phrases and/or words indicating positiveand/or negative indicators that are stored in memory at computingdevice, or the like.

Still referring to FIG. 1 , a language processing module and/ordiagnostic engine may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input terms and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted words, phrases, and/orother semantic units. There may be a finite number of categories towhich an extracted word may pertain; an HIM inference algorithm, such asthe forward-backward algorithm or the Viterbi algorithm, may be used toestimate the most likely discrete state given a word or sequence ofwords. Language processing module may combine two or more approaches.For instance, and without limitation, machine-learning program may use acombination of Naïve-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 1 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Now referring to FIG. 2 , an exemplary embodiment of an ingredientdatabase 200 is presented. Ingredient database 200 may include anydatabase as described above with reference to FIG. 1 . Ingredientdatabase 200 may be include a database. Database may be implemented,without limitation, as a relational database, a key-value retrievaldatabase such as a NOSQL database, or any other format or structure foruse as a database that a person skilled in the art would recognize assuitable upon review of the entirety of this disclosure. Database mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Database may include a plurality of data entries and/orrecords as described above. Data entries in a database may be flaggedwith or linked to one or more additional elements of information, whichmay be reflected in data entry cells and/or in linked tables such astables related by one or more indices in a relational database. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a database may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure.

Still referring to FIG. 2 , in some embodiments, ingredient database 200may include user selection data table 204. User selection data table 204may include, but is not limited to, diet data, geographical data,exercise data, product usage data, product purchasing data, and thelike. User selection data table 204 may include any of the physiologicaldata as described above with reference to FIG. 1 .

Still referring to FIG. 2 , in some embodiments, ingredient database 200may include ingredients data table 208. Ingredients data table 208 mayinclude one or more ingredient used to comprise user selection 108. Insome embodiments, ingredients data may include, without limitation,sources of ingredients, amounts of ingredients, types of ingredients,and the like.

Still referring to FIG. 2 , in some embodiments, ingredient database 200may include nutrients data table 212. Nutrients data table 212 mayinclude, but is not limited to, types of nutrients in each ingredient,quantities of nutrients, frequency of consumption of nutrients, seasonalnutrients in the area, and the like. Plurality of ingredients 112 may beas described above with reference to FIG. 1 .

Still referring to FIG. 2 , in some embodiments, ingredient database 200may include biodiversity score data table 216. Biodiversity score datatable 216 may include, but is not limited to, levels of biodiversity inthe user's microbiome, scores from previous user selections, predictedchanges in biodiversity, and the like. Nutrient biodiversity score 120may be as described above with reference to FIG. 1 .

Referring to FIG. 3 , an exemplary embodiment of fuzzy set comparison300 is illustrated. A first fuzzy set 304 may be represented, withoutlimitation, according to a first membership function 308 representing aprobability that an input falling on a first range of values 312 is amember of the first fuzzy set 304, where the first membership function308 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function308 may represent a set of values within first fuzzy set 304. Althoughfirst range of values 312 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 312 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 308 mayinclude any suitable function mapping first range 312 to a probabilityinterval, including without limitation a triangular function defined bytwo linear elements such as line segments or planes that intersect at orbelow the top of the probability interval. As a non-limiting example,triangular membership function may be defined as:

${y\left( {x,\ a,\ b,\ c} \right)} = \left\{ \begin{matrix}{0,\ {{{for}x} > {c{and}x} < a}} \\{\frac{x - a}{b - a},\ {{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},\ {{{if}b} < x \leq c}}\end{matrix} \right.$

a trapezoidal membership function may be defined as:

${y\left( {x,\ a,\ b,\ c,\ d} \right)} = {\max\left( {{\min\ \left( {\frac{x - a}{b - a},\ 1,\frac{d - x}{d - c}} \right)},\ 0} \right)}$

a sigmoidal function may be defined as:

${y\left( {x,\ a,\ c} \right)} = \frac{1}{1 - e^{- {a({x - c})}}}$

a Gaussian membership function may be defined as:

${y\left( {x,\ c,\ \sigma} \right)} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$

and a bell membership function may be defined as:

${y\left( {x,\ a,\ b,\ c,} \right)} = \left\lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \right\rbrack^{- 1}$

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 3 , first fuzzy set 304 may represent any valueor combination of values as described above, including output from oneor more machine-learning models and articles of interest, apredetermined class, such as without limitation a toxic load quantifier.A second fuzzy set 316, which may represent any value which may berepresented by first fuzzy set 304, may be defined by a secondmembership function 320 on a second range 324; second range 324 may beidentical and/or overlap with first range 312 and/or may be combinedwith first range via Cartesian product or the like to generate a mappingpermitting evaluation overlap of first fuzzy set 304 and second fuzzyset 316. Where first fuzzy set 304 and second fuzzy set 316 have aregion 328 that overlaps, first membership function 308 and secondmembership function 320 may intersect at a point 332 representing aprobability, as defined on probability interval, of a match betweenfirst fuzzy set 304 and second fuzzy set 316. Alternatively oradditionally, a single value of first and/or second fuzzy set may belocated at a locus 336 on first range 312 and/or second range 324, wherea probability of membership may be taken by evaluation of firstmembership function 308 and/or second membership function 320 at thatrange point. A probability at 328 and/or 332 may be compared to athreshold 340 to determine whether a positive match is indicated.Threshold 340 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 304 and second fuzzy set 316, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or an article of interest and apredetermined class, such as without limitation a toxic load quantifierfor combination to occur as described above. Alternatively oradditionally, each threshold may be tuned by a machine-learning and/orstatistical process, for instance and without limitation as described infurther detail below.

Further referring to FIG. 3 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify an article of interest with atoxic load quantifier. For instance, if an article of interest has afuzzy set matching a toxic load quantifier fuzzy set by having a degreeof overlap exceeding a threshold, apparatus 100 may classify the articleof interest as belonging to the toxic load quantifier fuzzy set. Wheremultiple fuzzy matches are performed, degrees of match for eachrespective fuzzy set may be computed and aggregated through, forinstance, addition, averaging, or the like, to determine an overalldegree of match.

Still referring to FIG. 3 , in an embodiment, an article of interest maybe compared to multiple toxic load quantifier fuzzy sets. For instance,an article of interest may be represented by a fuzzy set that iscompared to each of the multiple toxic load quantifier fuzzy sets; and adegree of overlap exceeding a threshold between the article of interestfuzzy set and any of the multiple toxic load quantifier fuzzy sets maycause apparatus 100 to classify the article of interest as belonging toa toxic load quantifier fuzzy set. For instance, in one embodiment theremay be two toxic load quantifier fuzzy sets, representing respectively ahigh toxic load quantifier fuzzy set and a moderate toxic loadquantifier fuzzy set. A high toxic load quantifier fuzzy set may have afirst fuzzy set; a moderate toxic load quantifier fuzzy set may have asecond fuzzy set; and articles of interest may have an articles ofinterest fuzzy set. Apparatus 100, for example, may compare an articleof interest fuzzy set with each of a high toxic load quantifier fuzzyset and a moderate toxic load quantifier fuzzy set, as described above,and classify an article of interest to either, both, or neither of ahigh toxic load quantifier fuzzy set or a moderate toxic load quantifierfuzzy set. Machine-learning methods as described throughout may, in anon-limiting example, generate coefficients used in fuzzy set equationsas described above, such as without limitation x, c, and a of a Gaussianset as described above, as outputs of machine-learning methods.Likewise, an article of interest may be used indirectly to determine afuzzy set, as an article of interest fuzzy set may be derived fromoutputs of one or more machine-learning models that take the article ofinterest directly or indirectly as inputs.

Still referring to FIG. 3 , a processor may use a logic comparisonprogram, such as, but not limited to, a fuzzy logic model to determine atoxic load quantifier. A toxic load quantifier may include, but is notlimited to, low, average, high, and the like; each such toxic loadquantifier may be represented as a value for a linguistic variablerepresenting a toxic load quantifier or in other words a fuzzy set asdescribed above that corresponds to a degree of toxic load as calculatedusing any statistical, machine-learning, or other method that may occurto a person skilled in the art upon reviewing the entirety of thisdisclosure. In other words, a given element of an article of interestmay have a first non-zero value for membership in a first linguisticvariable value such as “1” and a second non-zero value for membership ina second linguistic variable value such as “2”. In some embodiments,determining a toxic load quantifier may include using a linearregression model. A linear regression model may include a machinelearning model. A linear regression model may be configured to map dataof an article of interest and/or user data, such as ingredients of anarticle of interest, to one or more toxic load quantifiers. A linearregression model may be trained using training data correlating articlesof interest to toxic load quantifiers. A linear regression model may mapstatistics such as, but not limited to, toxic load impact magnitude,frequency of articles of interest of toxic load quantifiers, and thelike. In some embodiments, determining a toxic load quantifier of anarticle of interest may include using a toxic load quantifierclassification model. A toxic load quantifier classification model maybe configured to input collected data and cluster data to a centroidbased on, but not limited to, frequency of appearance, linguisticindicators of toxic elements, and the like. Centroids may include scoresassigned to them such that elements of articles of interest may each beassigned a score. In some embodiments, a toxic load quantifierclassification model may include a K-means clustering model. In someembodiments, a toxic load quantifier classification model may include aparticle swarm optimization model. In some embodiments, determining atoxic load quantifier of an article of interest may include using afuzzy inference engine. A fuzzy inference engine may be configured tomap one or more article of interest data elements using fuzzy logic. Insome embodiments, a plurality of entity assessment devices may bearranged by a logic comparison program into toxic load quantifierarrangements. A “toxic load quantifier arrangement” as used in thisdisclosure is any grouping of objects and/or data based on skill leveland/or output score. This step may be implemented as described above inFIGS. 1-3 and below in FIG. 5 . Membership function coefficients and/orconstants as described above may be tuned according to classificationand/or clustering algorithms. For instance, and without limitation, aclustering algorithm may determine a Gaussian or other distribution ofquestions about a centroid corresponding to a given toxic load level,and an iterative or other method may be used to find a membershipfunction, for any membership function type as described above, thatminimizes an average error from the statistically determineddistribution, such that, for instance, a triangular or Gaussianmembership function about a centroid representing a center of thedistribution that most closely matches the distribution. Error functionsto be minimized, and/or methods of minimization, may be performedwithout limitation according to any error function and/or error functionminimization process and/or method as described in this disclosure.

Further referring to FIG. 3 , an inference engine may be implementedaccording to input and/or output membership functions and/or linguisticvariables. For instance, a first linguistic variable may represent afirst measurable value pertaining to elements of an article of interestsuch as a degree of toxicity of an element of an article of interest,while a second membership function may indicate a degree of toxic loadimpact of a subject thereof, or another measurable value pertaining toan article of interest. Continuing the example, an output linguisticvariable may represent, without limitation, a score value. An inferenceengine may combine rules, such as: “if the article of interest has ‘highamounts of a toxic element’ and the usage frequency is ‘high’, the toxicload quantifier is ‘high’”—the degree to which a given input functionmembership matches a given rule may be determined by a triangular normor “T-norm” of the rule or output membership function with the inputmembership function, such as min (a, b), product of a and b, drasticproduct of a and b, Hamacher product of a and b, or the like, satisfyingthe rules of commutativity (T(a, b)=T(b, a)), monotonicity: (T(a,b)≤T(c, d) if a≤c and b≤d), (associativity: T(a, T(b, c))=T(T(a, b),c)), and the requirement that the number 1 acts as an identity element.Combinations of rules (“and” or “or” combination of rule membershipdeterminations) may be performed using any T-conorm, as represented byan inverted T symbol or “l,” such as max(a, b), probabilistic sum of aand b (a+b−a*b), bounded sum, and/or drastic T-conorm; any T-conorm maybe used that satisfies the properties of commutativity: ⊥(a, b)=⊥(b, a),monotonicity: ⊥(a, b)≤⊥(c, d) if a≤c and b≤d, associativity: ⊥(a, ⊥(b,c))=⊥(⊥(a, b), c), and identity element of 0. Alternatively oradditionally T-conorm may be approximated by sum, as in a “product-sum”inference engine in which T-norm is product and T-conorm is sum. A finaloutput score or other fuzzy inference output may be determined from anoutput membership function as described above using any suitabledefuzzification process, including without limitation Mean of Maxdefuzzification, Centroid of Area/Center of Gravity defuzzification,Center Average defuzzification, Bisector of Area defuzzification, or thelike. Alternatively or additionally, output rules may be replaced withfunctions according to the Takagi-Sugeno-King (TSK) fuzzy model.

Further referring to FIG. 3 , an article of interest to be used may beselected by user selection, and/or by selection of a distribution ofoutput scores, such as 30% low toxic load quantifiers, 40% moderatetoxic load quantifiers, and 30% high toxic load quantifiers or the like.Each ranking may be selected using an additional function such as adegree of toxicity as described above.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample [describe inputs and outputs that might be used with invention].

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”as described herein, neural net, or program generated by a machinelearning algorithm known as a “classification algorithm,” as describedin further detail below, that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. A classifier may be configured to output at least a datumthat labels or otherwise identifies a set of data that are clusteredtogether, found to be close under a distance metric as described below,or the like. Machine-learning module 400 may generate a classifier usinga classification algorithm, defined as a processes whereby a computingdevice and/or any module and/or component operating thereon derives aclassifier from training data 404. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naïve Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. As a non-limiting example, training dataclassifier 416 may classify elements of training data to [something thatcharacterizes a sub-population, such as a cohort of persons and/or otheranalyzed items and/or phenomena for which a subset of training data maybe selected].

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude [input examples] as described above as inputs, [output examples]as outputs, and a scoring function representing a desired form ofrelationship to be detected between inputs and outputs; scoring functionmay, for instance, seek to maximize the probability that a given inputand/or combination of elements inputs is associated with a given outputto minimize the probability that a given input is not associated with agiven output. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in training data 404.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of at least asupervised machine-learning process 428 that may be used to determinerelation between inputs and outputs. Supervised machine-learningprocesses may include classification algorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Now referring to FIG. 5 , a flow diagram for a method 500 for adjustinga user nourishment selection based on nutrient diversity is shown.Method 500 is performed by at least a processor 104. At least aprocessor 104 may be any of the processors or computing devices asdescribed herein with reference to FIGS. 1 and 6 .

Still referring to FIG. 5 , at step 505, method 500 includes receiving,at the at least a processor 104, a proposed user selection 108 relatingto nourishment, wherein proposed user selection 108 comprises aplurality of ingredients 112. Receiving proposed user selection 108relating to nourishment may include receiving a user flavor preference.Proposed user selection 108 may be received as a function of abiological extraction. At least a processor 104 may be any of theprocessors or computing devices as described herein with reference toFIGS. 1 and 6 . Proposed user selection 108 may be any of the data asdescribed herein with reference to FIGS. 1 and 2 . Plurality ofingredients 112 may be any of the ingredients as described herein withreference to FIGS. 1 and 2 .

Still referring to FIG. 5 , at step 510, method 500 includes evaluating,at the at least a processor 104, each ingredient of plurality ofingredients 112. At least a processor 104 may be any of the processorsor computing devices as described herein with reference to FIGS. 1 and 6. Plurality of ingredients 112 may be any of the ingredients asdescribed herein with reference to FIGS. 1 and 2 .

Continuing to refer to FIG. 5 , evaluating each ingredient includesextracting at least a nutrient 116 from each ingredient of the pluralityof ingredients 112 and calculating a nutrient biodiversity score 120 forthe at least a nutrient 116. Calculating a nutrient biodiversity score120 may further include the use of a biodiversity machine-learning model124. Calculating a nutrient biodiversity score 120 may further includethe use of a nutrient classifier 132. Calculating nutrient biodiversityscore 120 may comprise retrieving, at the at least a processor 104, atleast a nutrient 116 containing a logged biodiversity entry andgenerating, at the at least a processor 104, a biodiversitymachine-learning model 124, wherein the biodiversity machine-learningmodel utilizes the logged biodiversity entry as an input, and outputsthe nutrient biodiversity score 120. Nutrient biodiversity score 120represents the effect of the at least a nutrient 116 on the diversity ofa microbiome. Plurality of ingredients 112 may be any of the ingredientsas described herein with reference to FIGS. 1 and 2 . At least anutrient 116 may be any of the nutrients as described herein withreference to FIGS. 1 and 2 . Nutrient biodiversity score 120 may be anyof the scores as described herein with reference to FIGS. 1 and 2 .

Still referring to FIG. 5 , at step 515, method 500 includes optimizing,at the at least a processor 104, plurality of ingredients 112 as afunction of each nutrient biodiversity score 120. Optimizing pluralityof ingredients 112 includes creating, at the at least a processor 104, alist of the plurality of ingredients 112 and organizing, at the at leasta processor 104, the list of the plurality of ingredients 112 based oneach ingredient's biodiverse nutrient score 120. At least a processor104 may be any of the processors or computing devices as describedherein with reference to FIGS. 1 and 6 . Plurality of ingredients 112may be any of the ingredients as described herein with reference toFIGS. 1 and 2 . Nutrient biodiversity score 120 may be any of the scoresas described herein with reference to FIGS. 1 and 2 .

Still referring to FIG. 5 , at step 520, method 500 includes adjusting,at the at least a processor 104, plurality of ingredients 112 as afunction of the optimization of the plurality of ingredients 112.Adjusting plurality of ingredients 112 may include replacing at least aningredient of the plurality of ingredients 112. Adjusting plurality ofingredients 112 may include maximizing the diversity of nutrients inplurality of ingredients 112. At least a processor 104 may be any of theprocessors or computing devices as described herein with reference toFIGS. 1 and 6 . Plurality of ingredients 112 may be any of theingredients as described herein with reference to FIGS. 1 and 2 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An apparatus for adjusting a user nourishment selection based onnutrient diversity, the apparatus comprising: a sensor configured to:detect hematological data of a user; store the hematological data as afunction of at least a signal from the sensor; and transmit thehematological data; at least a processor; and a memory communicativelyconnected to the at least a processor, the memory configured to storethe hematological data and containing instructions configuring the atleast a processor to: receive a proposed user selection relating tonourishment, wherein the proposed user selection comprises a pluralityof ingredients and the hematological data of the user; evaluate eachingredient of the plurality of ingredients, wherein evaluating eachingredient includes: extracting at least a nutrient from each ingredientof the plurality of ingredients; and calculating a nutrient biodiversityscore associated with the user for the at least a nutrient, whereincalculating the nutrient biodiversity score comprises: classifyingbiodiversity training data, including a plurality of user selection dataand a plurality of biodiversity nutrient score data, into categoriesusing a fuzzy inference system by formulating mappings between elementsof the plurality of user selection data and the plurality ofbiodiversity nutrient score data using fuzzy logic; training abiodiversity machine-learning model using the classified biodiversitytraining data; and generating the nutrient biodiversity score associatedwith the user using the biodiversity machine learning model by providingthe at least a nutrient and the hematological data of the user as inputsto the trained biodiversity machine-learning model; optimize theplurality of ingredients as a function of each nutrient biodiversityscore; and adjust the plurality of ingredients as a function of theoptimization of the plurality of ingredients.
 2. The apparatus of claim1, wherein receiving the proposed user selection relating to nourishmentincludes receiving a user flavor preference.
 3. (canceled)
 4. Theapparatus of claim 1, wherein calculating the nutrient biodiversityscore further includes the use of a biodiversity classifier.
 5. Theapparatus of claim 1, wherein calculating the nutrient biodiversityscore includes: retrieving at least a nutrient containing a loggedbiodiversity entry; and generating the nutrient biodiversity score byutilizing the logged biodiversity entry as an input to the biodiversitymachine-learning model.
 6. The apparatus of claim 1, wherein thenutrient biodiversity score represents the effect of the at least anutrient on a diversity of a microbiome.
 7. The apparatus of claim 1,wherein optimizing the plurality of ingredients includes: creating alist of the plurality of ingredients; and organizing the list of theplurality of ingredients based on each ingredient of the plurality ofingredient's nutrient biodiversity score.
 8. The apparatus of claim 1,wherein adjusting the plurality of ingredients includes replacing atleast an ingredient of the plurality of ingredients.
 9. The apparatus ofclaim 1, wherein adjusting the plurality of ingredients includesmaximizing the diversity of nutrients in the plurality of ingredients.10. The apparatus of claim 1, wherein the proposed user selection isreceived as a function of a biological extraction.
 11. A method foradjusting a user nourishment selection based on nutrient diversity, themethod comprises: detecting, at a sensor, hematological data of a user;storing, at the sensor, the hematological data as a function of at leasta signal from the sensor; and transmitting, at the sensor, thehematological data; receiving, at a processor, a proposed user selectionrelating to nourishment, wherein the proposed user selection comprises aplurality of ingredients and the hematological data of the user;evaluating, at the processor, each ingredient of the plurality ofingredients, wherein evaluating each ingredient includes: extracting atleast a nutrient from each ingredient of the plurality of ingredients;and calculating a nutrient biodiversity score associated with the userfor the at least a nutrient, wherein calculating the nutrientbiodiversity score comprises: classifying biodiversity training data,including a plurality of user selection data and a plurality ofbiodiversity nutrient score data, into categories using a fuzzyinference system by formulating mappings between elements of theplurality of user selection data and the plurality of biodiversitynutrient score data using fuzzy logic: training a biodiversitymachine-learning model using the classified biodiversity training data;and generating the nutrient biodiversity score associated with the userusing the biodiversity machine learning model by providing the at leasta nutrient and the hematological data of the user as inputs to thetrained biodiversity machine-learning model; optimizing, at theprocessor, the plurality of ingredients as a function of each nutrientbiodiversity score; and adjusting, at the processor, the plurality ofingredients as a function of the optimization of the plurality ofingredients.
 12. The method of claim 11, wherein receiving the proposeduser selection relating to nourishment includes receiving a user flavorpreference.
 13. (canceled)
 14. The method of claim 11, whereincalculating the nutrient biodiversity score further includes the use ofa biodiversity classifier.
 15. The method of claim 11, whereincalculating the nutrient biodiversity score includes: retrieving atleast a nutrient containing a logged biodiversity entry; and generatingthe nutrient biodiversity score by utilizing the logged biodiversityentry as an input to the biodiversity machine-learning model.
 16. Themethod of claim 11, wherein the nutrient biodiversity score representsthe effect of the at least a nutrient on a diversity of a microbiome.17. The method of claim 11, wherein optimizing the plurality ofingredients includes: creating a list of the plurality of ingredients;and organizing the list of the plurality of ingredients based on eachingredient of the plurality of ingredient's nutrient biodiversity score.18. The method of claim 11, wherein adjusting the plurality ofingredients includes replacing at least an ingredient of the pluralityof ingredients.
 19. The method of claim 11, wherein adjusting theplurality of ingredients includes maximizing the diversity of nutrientsin the plurality of ingredients.
 20. The method of claim 11, wherein theproposed user selection is received as a function of a biologicalextraction.
 21. The apparatus of claim 1, wherein: the sensor iscommunicatively connected to the at least a processor and the memory;the sensor comprises a wearable monitoring device that contacts the userduring at least the detection of the hematological data of the user; thehematological data comprises information relating to a nutrientdeficiency of the user; and formulating the mappings between theelements of the plurality of user selection data and the plurality ofbiodiversity nutrient score data using fuzzy logic comprises determininga degree of overlap between at least a first fuzzy set representative ofthe elements of the plurality of user selection data and at least asecond fuzzy set representative of the elements of the plurality ofbiodiversity nutrient score data.
 22. The method of claim 11, wherein:the sensor is communicatively connected to the at least a processor andthe memory; the sensor comprises a wearable monitoring device thatcontacts the user during at least the detection of the hematologicaldata of the user; the hematological data comprises information relatingto a nutrient deficiency of the user; and formulating the mappingsbetween the elements of the plurality of user selection data and theplurality of biodiversity nutrient score data using fuzzy logiccomprises determining a degree of overlap between at least a first fuzzyset representative of the elements of the plurality of user selectiondata and at least a second fuzzy set representative of the elements ofthe plurality of biodiversity nutrient score data.